




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__			: str    =  {}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__			: int    =  ['NllbTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__			: Union[str, Any]    =  ['NllbTokenizerFast']


if TYPE_CHECKING:
       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_nllb import NllbTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_nllb_fast import NllbTokenizerFast

else:
       import sys

       SCREAMING_SNAKE_CASE__			: List[Any]    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
